Compute TF module enrichment using directed target genes
compute.modules.enrichment.RdThis function performs enrichment analysis for transcription factor (TF) modules using known TF-target interactions. For each module, it identifies hub TFs and retrieves their known targets from the CollecTRI database. It then conducts an over-representation analysis (ORA) against the Reactome pathway database. To reduce redundancy, only unique pathways per module are retained by filtering out overlaps between modules.
Arguments
- RNA.tpm
A numeric matrix of normalized gene expression values. Rows are gene symbols, columns are samples. This matrix is used to restrict the universe of genes used in enrichment and validate TF-target gene presence.
- hub_tfs
A list of hub TFs per module, typically obtained using
identify_hub_TFs(). This should be a list of two elements: one with named hub TFs per module, and another with their module membership or additional metadata.
Value
No object is returned. For each module with significant enrichment (p-value < 0.05),
a dot plot is saved in the Results/ directory as a PDF file named
Module <color>.pdf. If no enrichment is found for a module, a message is printed
and no file is saved for that module.
Details
The function uses the decoupleR::get_collectri() function to obtain TF-target relationships,
and clusterProfiler::enrichPathway() for ORA using the Reactome database.
Pathways shared between multiple modules are filtered using a Venn diagram-based comparison
to retain only module-specific results.
Examples
if (FALSE) { # \dontrun{
# Identify hub TFs from a TF activity matrix and WGCNA network
hub_tfs <- identify_hub_TFs(t(tfs_activity), wgcna_network, MM_thresh = 0.8, degree_thresh = 0.9)
# Perform module-level Reactome enrichment
compute.modules.enrichment(counts.norm, hub_tfs)
} # }